Gavin Giovannoni, FRCP, PhD,5Andrew J. Lees, FRCP, MD,1,2and John Hardy, PhD*1,2
1Reta Lila Weston Institute, UCL Institute of Neurology 2Department of Molecular Neuroscience, UCL Institute of
Neurology
3Department of Clinical Neuroscience, UCL Institute of Neurology
4Wolfson Institute of Preventive Medicine, Queen Mary University of London
5Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London
References
1. Sieber B-A, Landis S, Koroshetz W, et al. Prioritized research rec-ommendations from the National Institute of Neurological Disor-ders and Stroke Parkinson’s Disease 2014 conference. Ann Neurol 2014;76:469-472.
2. Noyce AJ, Bestwick JP, Silveira-Moriyama L, et al. Meta-analysis of early nonmotor features and risk factors for Parkinson disease. Ann Neurol 2012;72:893-901.
3. Noyce AJ, Bestwick JP, Silveira-Moriyama L, et al. PREDICT-PD: identifying risk of Parkinson’s disease in the community: methods and baseline results. J Neurol Neurosurg Psychiatry 2014;85:31-37. 4. Winder-Rhodes SE, Evans JR, Ban M, et al. Glucocerebrosidase
mutations influence the natural history of Parkinson’s disease in a community-based incident cohort. Brain 2013;136:392-399. 5. Duran R, Mencacci NE, Angeli AV, et al. The glucocerobrosidase
E326K variant predisposes to Parkinson’s disease, but does not cause Gaucher’s disease. Mov Disord 2012;28:232-236.
6. Walker JM, Lwin A, Tayebi N, et al. Glucocerebrosidase mutation T369M appears to be another polymorphism. Clin Genet 2003;63: 237-238.
7. Healy DG, Falchi M, O’Sullivan SS, et al. Phenotype, genotype, and worldwide genetic penetrance of LRRK2-associated Parkin-son’s disease: a case-control study. Lancet Neurol 2008;7:583-590.
Supporting Data
Additional Supporting Information may be found in the online version of this article.
Adaptive Deep Brain
Stimulation in a Freely
Moving Parkinsonian
Patient
The future of deep brain stimulation (DBS) for Parkinson’s disease (PD) lies in new closed-loop systems that continuously supply the implanted stimulator with new settings obtained by analyzing a feedback signal related to the patient’s current clinical condition.1The most suitable feedback for PD is sub-thalamic local field potential (LFP) activity recorded from the stimulating electrode itself.2-4 This closed-loop technology known as adaptive DBS (aDBS) recently proved superior to conventional open-loop DBS (cDBS) in patients with PD.2
No studies have yet tested aDBS in freely moving humans for a prolonged time. This information is an essential
prereq-uisite for developing new implantable aDBS devices for chronic PD treatment.
In this single-case study, we tested whether a portable DBS device we developed is suitable to compare the clinical benefit in a freely moving PD patient induced by either aDBS or cDBS. To do so, after a first experimental session for extracting patient settings to personalize the aDBS algo-rithm, we treated a blinded patient (51 y old, male, 8 y PD history) with cDBS and aDBS in two separate experimental sessions each lasting 120 min, 5 and 6 d, respectively, after DBS electrode implant. To ensure reliable results, the patient underwent repeated clinical assessments every 20 min (T1-T5) by two independent blinded neurologists through Uni-fied Parkinson’s Disease Rating Scale (UPDRS) III subsec-tions and Rush Dyskinesia Rating Scale (see Supplemental Data for details).
The aDBS portable device we used was equipped with an ad hoc algorithm that analyzed patient’s LFP beta band power (13-17 Hz) and adapted voltage stimulation linearly each sec-ond (Fig. 1A).
The patient during aDBS experienced a more stable con-dition than during cDBS, with better control of symptoms and dyskinesias over time (Fig. 1; video 1). In particular, aDBS and cDBS improved patient’s axial symptoms to a similar extent (Fig. 1B), but compared with cDBS, aDBS sig-nificantly improved his main symptom, bradykinesia (Fig. 1C). aDBS did not elicit side effects and was well tolerated.
Because we evaluated the patient a few days after surgery when he probably manifested a stunning effect,5the aDBS-and cDBS-induced improvements were lower than those reported by others in follow-up DBS studies.6A major clini-cal achievement was that compared with cDBS, aDBS greatly reduced the patient’s dyskinesias during gait and at rest (Fig. 1B; Fig. 1D). Presumably it did so because we designed the adaptive algorithm to avoid dyskinesias related to hyperstimulation: when L-dopa reduced beta-band LFP
activity, the voltage linearly diminished, avoiding hyperstimulation.
---VC2014 The Authors. Movement Disorders published by Wiley Periodi-cals, Inc. on behalf of International Parkinson and Movement Disorder Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and repro-duction in any medium, provided the original work is properly cited. *Correspondence to: Dr. Manuela Rosa, MS, Universita degli Studi di Milano, Centro Clinico per la Neurostimolazione, le Neurotecnologie ed i Disordini del Movimento Fondazione IRCCS, Ca’ Granda Ospedale Mag-giore Policlinico, Via Francesco Sforza 35, Milano, 20122 Italy, e-mail: manuela.rosa@policlinico.mi.it
Funding agencies: This study was supported by Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico and by Universita degli Studi di Milano (Italy).
Relevant conflicts of interest/financial disclosures: Filippo Cogiama-nian, Sara Marceglia, Paolo M Rampini e Alberto Priori are shareholders of Newronika s.r.l., a spin-off company of Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan and Universita degli Studi di Milano. Full financial disclosures and author roles may be found in the online ver-sion of this article.
Received: 6 October 2014; Revised: 20 February 2015; Accepted: 21 March 2015
Published online 21 May 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/mds.26241
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Our results, besides corroborating findings reported by Lit-tle and colleagues2showing that aDBS promises to be more efficient and effective than cDBS, expand them for two impor-tant reasons. First, we tested aDBS for a longer observation time than Little et al., and in a more ecological condition (freely moving patient). Second, the personalized algorithm continuously adapts stimulation settings according to LFP beta changes, instead of providing an on–off strategy.
The aDBS device we used here can assess large patient series in real clinical settings, testing different LFP-based adaptive strategies other than those controlled by the beta activity to find the frequency that is more suitable to reflect patient clinical state.7
In conclusion, the approach and device we used proved eligible for prolonged use in a freely moving parkinsonian patient and disclosed new opportunities to study aDBS during patients’ daily activities, providing new insights into how this novel DBS strategy should improve patients’ quality of life. Although we await future studies to con-firm our findings and to test other aDBS LFP-based algo-rithms, our observation is a step toward developing a new
generation of implantable aDBS devices for chronic treat-ment of PD.
Video legend
Video: The video shows a section of patient clinical assess-ments performed 120 min after the experiment began (T5) during standard DBS (cDBS) on the left and during adaptive DBS (aDBS) on the right. Standard DBS was delivered at 2 V, 130 Hz, 60 ms; aDBS was delivered at a stimulation volt-age that automatically changes according to the online LFP beta recording analysis (voltage range, 0-2 V), 130 Hz, 60 ms. The video shows the patient during the execution of items 29, 23, 24, and 31 of unified parkinson’s disease rat-ing scale (UPDRS) III scale.
Manuela Rosa, MS,*1,2Mattia Arlotti, MS,1,3Gianluca Ardo-lino, MD,1Filippo Cogiamanian, MD,1Sara Marceglia, MS, PhD,1Alessio Di Fonzo, MD, PhD,1Francesca Cortese, MD,1,2,4
Paolo M. Rampini, MD,1and Alberto Priori, MD, PhD1,2 FIG. 1. (A) Sample of aDBS functioning lasting 10 min. Upper panel, the local field potential (LFP) beta band (13-17 Hz) power and below the stimu-lation voltage. The dotted line represents the time levodopa (L-dopa) took to achieve its clinical effect. The voltage delivered by aDBS followed the beta-band changes: WhenL-dopa reduced beta-band LFP activity, the voltage linearly diminished. (B) Clinical results for axial symptoms and dyski-nesias during gait. Mean Unified Parkinson’s Disease Rating Scale (UPDRS) III subsection (items 28, 29, 30) and mean Rush Dyskidyski-nesias Rating Scale (DRS) (during gait) percentage score changes from baseline evaluated at T5 (120 min after the experiment began) for cDBS and aDBS. Assessment at T5 showed that the patient’s axial symptoms improved to a similar extent after aDBS and cDBS, but dyskinesias during gait reduced more during aDBS than during cDBS. (C) Clinical results for bradykinesia. Mean changes from baseline in the UPDRS III subsection (items 23, 24, 31) percentage score changes from baseline for the upper limb contralateral to the stimulation side for cDBS and aDBS from T1 to T5. The UPDRS III subscore improved significantly more during aDBS than during cDBS (Wilcoxon matched pairs test; *P < 0.05). (D) Clinical results for dyskinesias at rest. Mean Rush DRS (at rest) percentage score changes from baseline for cDBS and aDBS from T1 to T5. Except at T3, aDBS induced a lower mean Rush DRS increase than cDBS (Wilcoxon matched pairs test; P > 0.05) (see Supplemental Data for data analysis details).
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1Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
2Universita degli Studi di Milano, Milan, Italy 3Department of Electrical, Electronic and Information
Engineer-ing ‘Guglielmo Marconi’, Universita di Bologna, Cesena, Italy 4Universita la Sapienza di Roma, Polo Pontino, Latina, Italy
Acknowledgments:We thank the neurosurgery staff for the precious collaboration: Marco Locatelli, Giorgio Carrabba and Vincenzo Levi.
References
1. Hebb AO, Zhang JJ, Mahoor MH, et al. Creating the feedback loop: closed-loop neurostimulation. Neurosurg Clin North Am 2014;25:187-204.
2. Little S, Pogosyan A, Neal S, et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann Neurol 2013;74:449-457. 3. Priori A, Foffani G, Rossi L, inventors. Apparatus for treating
neu-rological disorders by means of adaptive electro-stimulation retro-acted by biopotentials. U.P patent 8,078,281. 2005.
4. Priori A, Foffani G, Rossi L, Marceglia S. Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations. Exp Neurol 2013;245:77-86.
5. Mann JM, Foote KD, Garvan CW, et al. Brain penetration effects of microelectrodes and DBS leads in STN or GPi. J Neurol Neuro-surg Psychiatry 2009;80:794-797.
6. Deuschl G, Schade-Brittinger C, Krack P, et al. A randomized trial of deep-brain stimulation for Parkinson’s disease. N Engl J Med 2006;355:896-908.
7. Rosa M, Giannicola G, Marceglia S, Fumagalli M, Barbieri S, Priori A. Neurophysiology of deep brain stimulation. Int Rev Neu-robiol 2012;107:23-55.
Supporting Data
Additional supporting information may be found in the online version of this article at the publisher’s web-site.
Temporal Processing of
Perceived Body Movement
in Cervical Dystonia
Patients with idiopathic dystonia exhibit changes in the cognitive processing of movement.1-3 We showed that patients with writer’s cramp are less accurate than normal subjects in temporally predicting perceived handwriting.4 Whether this is selectively linked to the body area affected by dystonia or is a generalized cognitive feature of dystonia remains unclear. We addressed this issue by applying the same experimental paradigm to patients with focal cervical dystonia (CD).
Fifteen patients with focal CD, aged 56.2 6 13.9 y and treatment-free for at least 6 mo, and 15 age-matched healthy subjects were recruited in the Department of Neuroscience, University of Genoa. Patients’ disease duration was 9 6 6.5 y, and mean 6 standard deviation score on the Toronto Western Spasmodic Torticollis Rating Scale was 14.3 6 4.8. The experimental paradigm, previously published in Avan-zino et al.,4 consisted of the perception on a screen of two
videos, one showing a right hand writing a sentence (target task), and another showing a ball reaching a target (control task). After a variable interval from its onset (6, 9, and 12 seconds), videos were darkened. Subjects were asked to indi-cate when the perceived movement reached its end by click-ing on the keyboard space-bar (Supplemental Data). The timing error (Reproduced Interval – Dark Interval), the nor-malized absolute timing error ([Timing Error/Dark Interval] 3 100), and the coefficient of variability (standard devia-tion/mean of Reproduced Intervals) were measured and ana-lyzed with a repeated-measures analysis of variance with the factors GROUP, TASK, and DARK INTERVAL.
Repeated-measures analysis of variance showed a signifi-cant GROUP*TASK interaction only for the normalized absolute timing error (F[1,28] 5 5.85; P 5 0.022; Fig. 1). On post hoc, this parameter was greater at all dark intervals only in CD patients (P 5 0.006) and exclusively for the tar-get task (P 5 0.024).
In both groups of subjects, consistently with what was observed in our previous work,4 the ability to temporally predict the end of the perceived movement was influenced by the duration of the target interval and the type of motion. Shorter dark intervals were associated with a tendency to overestimate the duration of movement (F[2,56] 5 136.61; P < 0.001), greater variability (F[2,56] 5 50.60, P < 0.001), and greater absolute timing error (F[2,56] 5 26.06; P < 0.001). Finally, a tendency to overestimate the duration of movement was observed for the target task compared with the control task (F[1,28] 5 6.38, P 5 0.017). Absolute timing error did not correlate with disease severity (Spear-man’s rho 5 –0.161; P 5 0.58) or duration (Spear(Spear-man’s rho 5 0.040; P 5 0.89).
Our findings suggest that the abnormal timing of visually perceived human body motion is not exclusive to movements topographically related to dystonia. Brain regions relevant to the pathophysiology of dystonia, for example, sensorimotor regions of premotor and parietal cortices and cerebellum, modulate the spatiotemporal prediction of dynamic visual stimuli, and could be involved in the detected abnormal-ity.5,6 Despite the relatively small sample size, the lack of correlation between timing performance and severity/dura-tion of CD suggests that the observed abnormality may not be a direct expression of the dystonia.
The selectivity of the timing abnormality might depend in part on the difference in complexity between handwriting and the inanimate object motion. However, if motion com-plexity is the main determinant of implicit timing perform-ance, timing error should decrease at the increase of task complexity also in control subjects, but this was not observed. This notwithstanding, future studies should explore temporal processing of motion in dystonia, using
---*Correspondence to: Dr. Davide Martino, PhD, MD, International Parkin-son’s Centre of Excellence, King’s College and King’s College Hospital, Denmark Hill Campus, London, UK, E-mail: davidemartino@nhs.net Relevant conflicts of interest/financial disclosures: Nothing to report. Full financial disclosures and author roles may be found in the online ver-sion of this article.
Received: 11 December 2014; Revised: 5 March 2015; Accepted: 8 March 2015
Published online 16 April 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/mds.26225
L E T T E R S : N E W O B S E R V A T I O N S
